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Kegg

by io.github.pipeworx-io
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find
Read-onlyIdempotent

Search KEGG by keyword. KEGG is the authoritative bioinformatics database for compounds, drugs, diseases, metabolic pathways, genes, and enzymes. Pick a database (compound|drug|disease|pathway|genes|enzyme|glycan|module|ko) and pass a query like "glucose", "aspirin", or "diabetes". Returns matching KEGG IDs with descriptions. Keyless.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesSearch term, e.g. "glucose", "aspirin", "diabetes".
databaseYesOne of: compound, drug, disease, pathway, genes, enzyme, glycan, module, ko.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "database": "compound",
+    "query": "glucose"
+  },
+  {
+    "database": "drug",
+    "query": "aspirin"
+  },
+  {
+    "database": "disease",
+    "query": "diabetes"
+  }
+]
  2. First observed

TDQS

A4.4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds that it is 'keyless' (no API key) and returns KEGG IDs with descriptions, providing extra context beyond annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences plus a standalone 'Keyless.' which is efficient. Each sentence serves a purpose: purpose, domain context, examples, and auth info. No redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool is a simple search with 2 parameters and no output schema, the description explains the database options, query examples, and the return format (IDs with descriptions). It is sufficiently complete for an agent to use correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with descriptions for both parameters. The description reinforces this with concrete examples and a list of database values, making parameter usage clearer.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Search KEGG by keyword' and provides specific examples of databases and queries, making the purpose unmistakable and distinct from sibling tools like get_entry or search_within.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly lists acceptable databases and gives example queries, guiding when to use. However, it lacks explicit 'when not to use' or alternatives, though the context is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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